The multiscale interactive system composed of wind, leaves, and droplets serves as a critical dynamic unit in precision orchard spraying. Its coupling mechanisms fundamentally influence pesticide transport pathways, deposition patterns, and drift behavior within crop canopies, forming the foundational basis for achieving intelligent and site-specific spraying operations. This review systematically examines the synergistic dynamics across three hierarchical scales: Droplet–leaf surface wetting and adhesion at the microscale; leaf cluster motion responses at the mesoscale; and the modulation of airflow and spray plume diffusion by canopy architecture at the macroscale. Key variables affecting spray performance—such as wind speed and turbulence structure, leaf biomechanical properties, droplet size and electrostatic characteristics, and spatial canopy heterogeneity—are identified and analyzed. Furthermore, current advances in multiscale modeling approaches and their corresponding experimental validation techniques are critically evaluated, along with their practical boundaries of applicability. Results indicate that while substantial progress has been made at individual scales, significant bottlenecks remain in the integration of cross-scale models, real-time acquisition of critical parameters, and the establishment of high-fidelity experimental platforms. Future research should prioritize the development of unified coupling frameworks, the integration of physics-based and data-driven modeling strategies, and the deployment of multimodal sensing technologies for real-time intelligent spray decision-making. These efforts are expected to provide both theoretical foundations and technological support for advancing precision and intelligent orchard spraying systems.

In wind-assisted orchard spraying operations, the dynamic response of leaves—manifested through changes in their posture—critically influences droplet deposition on both sides of the leaf surface and the penetration depth into the canopy. These factors are pivotal in determining spray coverage and the spatial distribution of pesticide efficacy. However, current research lacks comprehensive quantification and correlation analysis of the temporal response characteristics of leaves under wind disturbances. To address this gap, a systematic analytical framework was proposed, integrating real-time leaf segmentation and tracking, geometric feature quantification, and statistical correlation modeling. High-frame-rate videos of fluttering leaves were acquired under controlled wind conditions, and background segmentation was performed using principal component analysis (PCA) followed by clustering in the reduced feature space. A fine-tuned Segment Anything Model 2 (SAM2-FT) was employed to extract dynamic leaf masks and enable frame-by-frame tracking. Based on the extracted masks, time series of leaf area and inclination angle were constructed. Subsequently, regression analysis, cross-correlation functions, and Granger causality tests were applied to investigate cooperative responses and potential driving relationships among leaves. Results showed that the SAM2-FT model significantly outperformed the YOLO series in segmentation accuracy, achieving a precision of 98.7% and recall of 97.48%. Leaf area exhibited strong linear coupling and directional causality, while angular responses showed weaker correlations but demonstrated localized synchronization. This study offers a methodological foundation for quantifying temporal dynamics in wind–leaf systems and provides theoretical insights for the adaptive control and optimization of intelligent spraying strategies.

For the problem that, under the soybean-maize strip intercropping pattern, the installation of anti-drift shields changes the mass and inertia characteristics of the boom of a shielded sprayer, making it prone to severe vibration under complex field excitations and thereby affecting application uniformity and operational safety, a semi-active boom vibration damping system based on a magnetorheological damper was designed and tested. First, an equivalent mechanical model of the boom considering added mass and stiffness variation was established, and the characteristics of excitation sources such as road spectra and start-stop impacts were clarified. Second, based on the Bouc–Wen model, the magnetorheological damper was selected and its output force boundaries were designed. The stroke was determined to be 80–110 mm, and the maximum damping forces in the compression and rebound strokes were 1.15 kN and 3.44 kN, respectively. Furthermore, a semi-active vibration damping system with an LK3U-14MT PLC as the core controller was developed. Finally, simulation analysis, free-vibration decay tests, obstacle-crossing tests, and field operation tests were conducted to verify the vibration damping performance of the developed semi-active system. The field test results showed that, at typical operating speeds of 3–5 km/h, the semi-active vibration damping system reduced the maximum vertical amplitude at the boom tip by 28.6–43.1% and the maximum inclination angle by 12.2–55.6%, effectively improving the attitude stability and roll resistance of the boom of the shielded sprayer.

Navigating hilly orchards is challenging for autonomous agricultural vehicles due to the rugged terrain and dense canopy cover. Standard environmental modeling techniques are widely used, yet they often overlook how elevation uncertainty propagates during Digital Elevation Model (DEM) reconstruction. This oversight can directly affect terrain risk assessments and navigation planning. From an error-propagation perspective, this review examines how uncertainties originating from RTK-GNSS, LiDAR, and computer vision propagate through DEM reconstruction, terrain-feature extraction, cost map construction, and path planning. We further analyze how DEM elevation errors and vertical inaccuracies affect slope estimation, roughness representation, traversability assessment, vehicle stability, and navigation safety. Finally, we highlight practical bottlenecks in hilly orchard scenarios and suggest several research priorities, including multimodal fusion, uncertainty-aware modeling, lifelong map updating, and learning-based traversability assessment.